Progressive structural deterioration of an endodontic instrument – A preliminary micro-computed tomography study

Barbosa, Igor Bastos; Scelza, Pantaleo; Pereira, António; Ferreira, Flávio; Bagueira, Rodrigo; Adeodato, Caroline Sousa Ribeiro; Scelza, Míriam Zaccaro

doi:10.1016/j.engfailanal.2019.05.029

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…In endodontics, AI and ML may predict periapical diseases, vertical root fracture, apical foramen, root morphologies, retreatment, and endodontic surgeries in the field of dentistry. [12][13][14][15][48][49][50][51][52] The current study includes the diagnosis of endodontic instrument failure. The typical endodontic RCT mechanics makes it laborious and prone to error in fault diagnosis estimation.…”

Section: Discussionmentioning

confidence: 99%

The implication of oversampling on the effectiveness of force signals in the fault detection of endodontic instruments during RCT

Thakur

Kankar

Parey

et al. 2023

Proc Inst Mech Eng H

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This work provides an innovative endodontic instrument fault detection methodology during root canal treatment (RCT). Sometimes, an endodontic instrument is prone to fracture from the tip, for causes uncertain the dentist’s control. A comprehensive assessment and decision support system for an endodontist may avoid several breakages. This research proposes a machine learning and artificial intelligence-based approach that can help to diagnose instrument health. During the RCT, force signals are recorded using a dynamometer. From the acquired signals, statistical features are extracted. Because there are fewer instances of the minority class (i.e. faulty/moderate class), oversampling of datasets is required to avoid bias and overfitting. Therefore, the synthetic minority oversampling technique (SMOTE) is employed to increase the minority class. Further, evaluating the performance using the machine learning techniques, namely Gaussian Naïve Bayes (GNB), quadratic support vector machine (QSVM), fine k-nearest neighbor (FKNN), and ensemble bagged tree (EBT). The EBT model provides excellent performance relative to the GNB, QSVM, and FKNN. Machine learning (ML) algorithms can accurately detect endodontic instruments’ faults by monitoring the force signals. The EBT and FKNN classifier is trained exceptionally well with an area under curve values of 1.0 and 0.99 and prediction accuracy of 98.95 and 97.56%, respectively. ML can potentially enhance clinical outcomes, boost learning, decrease process malfunctions, increase treatment efficacy, and enhance instrument performance, contributing to superior RCT processes. This work uses ML methodologies for fault detection of endodontic instruments, providing practitioners with an adequate decision support system.

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Section: Discussionmentioning

confidence: 99%

The implication of oversampling on the effectiveness of force signals in the fault detection of endodontic instruments during RCT

Thakur

Kankar

Parey

et al. 2023

Proc Inst Mech Eng H

View full text Add to dashboard Cite

show abstract

“…These approaches include micro-CT technology, optical micrograph, fractographic analysis, SEM, AFM, statistical features, etc. Barbosa et al 9 showed the internal and external defects of the endodontic instruments through micro-computed tomography. Additionally, they visualized the endodontic instrument's exterior surface and progressive deterioration.…”

Section: Introductionmentioning

confidence: 99%

Health prediction of reciprocating endodontic instrument based on the machine learning and exponential degradation models

Thakur,

Kankar,

Parey

et al. 2023

Proc Inst Mech Eng H

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This study proposes an intelligent health prediction and fault prognosis of the endodontic file during the root canal treatment. Root canal treatment is the procedure of disinfecting the infected pulp through the canal with the help of an endodontic instrument. Force signals are acquired with the help of a dynamometer during the canal preparation, and statistical features are extracted. The extracted features are selected through the window-wise feature extraction process. Characteristic features for endodontic file prognostics include time-domain features of the signals are evaluated. The extracted feature has inappropriate information, that is, noise between the signals; hence the smoothing of the feature is required at this stage to observe a trend in the signals. Based on the smoothing feature and post-processing of the feature, defined the health index to calculate the health condition of the endodontic instruments. A machine learning algorithm and exponential degradation model are used to predict the health of the endodontic instrument during the root canal treatment. This model is used to forecast the degradation of the endodontic file so that actions can be taken before actual failures happen. The proposed methodology can analyze the failures and micro-crack initiation of the endodontic instruments. Endodontics practitioners can use the machine learning models as well as an exponential model for estimating the health condition of the endodontic instrument. This study may help the clinician to progress the efficiency of the root canal treatment and the competence of the endodontic instruments.

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Noncontact 3D evaluation of surface topography of reciprocating instruments after retreatment procedures

et al. 2022

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This study evaluated the Reciproc R25 and Pro-R 25 instruments in unused condition, after one and a second use in endodontic retreatment employing a noncontact 3D light interferometer profiler, scanning electron microscopy (SEM) and cyclic fatigue tests. Twenty single-root teeth were instrumented with Reciproc R25 and filled with gutta-percha and sealer. A 3D profiler with a 20x objective using the Mx™ software was used to evaluate the cutting blade surfaces of Reciproc R25 and Pro-R 25 (n=5 per group) in unused condition, after the first and second uses in retreatment procedures. After retreatment, SEM was used to evaluate the topographic features of the used instruments. Cyclic fatigue tests were performed to compare new to used instruments. One-way ANOVA followed by Tukey test was used to compare the tested instruments before and after the first and second uses. Student t-test was used to compare the different instruments and for cyclic fatigue evaluation. No significant differences were observed in the cutting blade surfaces of Reciproc and Pro-R before and after one and two uses (p>0.05). Reciproc without use showed higher Sa and Sq when compared to Pro-R without use (p<0.05). No differences were observed between Reciproc and Pro-R after one and two uses (p>0.05). New and unused Reciproc showed longer time to fracture than Pro-R instruments (p<0.05), and only Pro-R showed differences between new and used instruments (p<0.05). Retreatment procedures with Reciproc and Pro-R did not change the surface topography of instruments. Reciproc had greater resistance to cyclic fatigue compared with Pro-R.

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Progressive structural deterioration of an endodontic instrument – A preliminary micro-computed tomography study

Cited by 4 publications

References 29 publications

The implication of oversampling on the effectiveness of force signals in the fault detection of endodontic instruments during RCT

The implication of oversampling on the effectiveness of force signals in the fault detection of endodontic instruments during RCT

Health prediction of reciprocating endodontic instrument based on the machine learning and exponential degradation models

Noncontact 3D evaluation of surface topography of reciprocating instruments after retreatment procedures

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